Communication stands as a potent mechanism to harmonize the behaviors of multiple agents. However, existing works primarily concentrate on broadcast communication, which not only lacks practicality, but also leads to information redundancy. This surplus, one-fits-all information could adversely impact the communication efficiency. Furthermore, existing works often resort to basic mechanisms to integrate observed and received information, impairing the learning process. To tackle these difficulties, we propose Targeted and Trusted Multi-Agent Communication (T2MAC), a straightforward yet effective method that enables agents to learn selective engagement and evidence-driven integration. With T2MAC, agents have the capability to craft individualized messages, pinpoint ideal communication windows, and engage with reliable partners, thereby refining communication efficiency. Following the reception of messages, the agents integrate information observed and received from different sources at an evidence level. This process enables agents to collectively use evidence garnered from multiple perspectives, fostering trusted and cooperative behaviors. We evaluate our method on a diverse set of cooperative multi-agent tasks, with varying difficulties, involving different scales and ranging from Hallway, MPE to SMAC. The experiments indicate that the proposed model not only surpasses the state-of-the-art methods in terms of cooperative performance and communication efficiency, but also exhibits impressive generalization.

翻译：通信是协调多智能体行为的一种有力机制。然而，现有工作主要关注广播通信，这不仅缺乏实用性，还会导致信息冗余。这种“一刀切”的冗余信息可能对通信效率产生负面影响。此外，现有工作常采用基础机制来整合观测与接收信息，从而损害学习过程。为解决这些难题，我们提出定向可信多智能体通信（T2MAC），一种简洁而有效的方法，使智能体能够学习选择性参与和证据驱动集成。借助T2MAC，智能体能够定制个性化消息、确定理想通信窗口并与可靠伙伴互动，从而提升通信效率。在接收消息后，智能体在证据层面上整合来自不同来源的观测与接收信息。该过程使智能体能够共同利用从多视角收集的证据，培养可信且协作的行为。我们在涵盖Hallway、MPE和SMAC等多种难度与规模的协作多智能体任务上评估了该方法。实验表明，所提模型不仅在协作性能与通信效率上超越现有最先进方法，还展现出卓越的泛化能力。